R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(25,0,23.6,0,22.3,0,21.8,0,20.8,0,19.7,0,18.3,0,17.4,0,17,0,18.1,0,23.9,0,25.6,0,25.3,0,23.6,0,21.9,0,21.4,0,20.6,0,20.5,0,20.2,0,20.6,0,19.7,0,19.3,0,22.8,0,23.5,0,23.8,0,22.6,0,22,0,21.7,0,20.7,0,20.2,0,19.1,0,19.5,0,18.7,0,18.6,0,22.2,0,23.2,0,23.5,0,21.3,0,20,0,18.7,0,18.9,0,18.3,0,18.4,0,19.9,0,19.2,0,18.5,0,20.9,1,20.5,1,19.4,1,18.1,1,17,1,17,1,17.3,1,16.7,1,15.5,1,15.3,1,13.7,1,14.1,1,17.3,1,18.1,1,18.1,1),dim=c(2,61),dimnames=list(c('werklozen','jobtonic'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('werklozen','jobtonic'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
werklozen jobtonic M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 25.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 23.6 0 0 1 0 0 0 0 0 0 0 0 0 2
3 22.3 0 0 0 1 0 0 0 0 0 0 0 0 3
4 21.8 0 0 0 0 1 0 0 0 0 0 0 0 4
5 20.8 0 0 0 0 0 1 0 0 0 0 0 0 5
6 19.7 0 0 0 0 0 0 1 0 0 0 0 0 6
7 18.3 0 0 0 0 0 0 0 1 0 0 0 0 7
8 17.4 0 0 0 0 0 0 0 0 1 0 0 0 8
9 17.0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 18.1 0 0 0 0 0 0 0 0 0 0 1 0 10
11 23.9 0 0 0 0 0 0 0 0 0 0 0 1 11
12 25.6 0 0 0 0 0 0 0 0 0 0 0 0 12
13 25.3 0 1 0 0 0 0 0 0 0 0 0 0 13
14 23.6 0 0 1 0 0 0 0 0 0 0 0 0 14
15 21.9 0 0 0 1 0 0 0 0 0 0 0 0 15
16 21.4 0 0 0 0 1 0 0 0 0 0 0 0 16
17 20.6 0 0 0 0 0 1 0 0 0 0 0 0 17
18 20.5 0 0 0 0 0 0 1 0 0 0 0 0 18
19 20.2 0 0 0 0 0 0 0 1 0 0 0 0 19
20 20.6 0 0 0 0 0 0 0 0 1 0 0 0 20
21 19.7 0 0 0 0 0 0 0 0 0 1 0 0 21
22 19.3 0 0 0 0 0 0 0 0 0 0 1 0 22
23 22.8 0 0 0 0 0 0 0 0 0 0 0 1 23
24 23.5 0 0 0 0 0 0 0 0 0 0 0 0 24
25 23.8 0 1 0 0 0 0 0 0 0 0 0 0 25
26 22.6 0 0 1 0 0 0 0 0 0 0 0 0 26
27 22.0 0 0 0 1 0 0 0 0 0 0 0 0 27
28 21.7 0 0 0 0 1 0 0 0 0 0 0 0 28
29 20.7 0 0 0 0 0 1 0 0 0 0 0 0 29
30 20.2 0 0 0 0 0 0 1 0 0 0 0 0 30
31 19.1 0 0 0 0 0 0 0 1 0 0 0 0 31
32 19.5 0 0 0 0 0 0 0 0 1 0 0 0 32
33 18.7 0 0 0 0 0 0 0 0 0 1 0 0 33
34 18.6 0 0 0 0 0 0 0 0 0 0 1 0 34
35 22.2 0 0 0 0 0 0 0 0 0 0 0 1 35
36 23.2 0 0 0 0 0 0 0 0 0 0 0 0 36
37 23.5 0 1 0 0 0 0 0 0 0 0 0 0 37
38 21.3 0 0 1 0 0 0 0 0 0 0 0 0 38
39 20.0 0 0 0 1 0 0 0 0 0 0 0 0 39
40 18.7 0 0 0 0 1 0 0 0 0 0 0 0 40
41 18.9 0 0 0 0 0 1 0 0 0 0 0 0 41
42 18.3 0 0 0 0 0 0 1 0 0 0 0 0 42
43 18.4 0 0 0 0 0 0 0 1 0 0 0 0 43
44 19.9 0 0 0 0 0 0 0 0 1 0 0 0 44
45 19.2 0 0 0 0 0 0 0 0 0 1 0 0 45
46 18.5 0 0 0 0 0 0 0 0 0 0 1 0 46
47 20.9 1 0 0 0 0 0 0 0 0 0 0 1 47
48 20.5 1 0 0 0 0 0 0 0 0 0 0 0 48
49 19.4 1 1 0 0 0 0 0 0 0 0 0 0 49
50 18.1 1 0 1 0 0 0 0 0 0 0 0 0 50
51 17.0 1 0 0 1 0 0 0 0 0 0 0 0 51
52 17.0 1 0 0 0 1 0 0 0 0 0 0 0 52
53 17.3 1 0 0 0 0 1 0 0 0 0 0 0 53
54 16.7 1 0 0 0 0 0 1 0 0 0 0 0 54
55 15.5 1 0 0 0 0 0 0 1 0 0 0 0 55
56 15.3 1 0 0 0 0 0 0 0 1 0 0 0 56
57 13.7 1 0 0 0 0 0 0 0 0 1 0 0 57
58 14.1 1 0 0 0 0 0 0 0 0 0 1 0 58
59 17.3 1 0 0 0 0 0 0 0 0 0 0 1 59
60 18.1 1 0 0 0 0 0 0 0 0 0 0 0 60
61 18.1 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) jobtonic M1 M2 M3 M4
24.69623 -3.23730 -0.04878 -1.32671 -2.49279 -2.97886
M5 M6 M7 M8 M9 M10
-3.40494 -3.95101 -4.69709 -4.42316 -5.26923 -5.17531
M11 t
-0.79393 -0.03393
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.6017 -0.3820 0.1983 0.5396 1.8295
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.69623 0.53240 46.387 < 2e-16 ***
jobtonic -3.23730 0.45627 -7.095 5.86e-09 ***
M1 -0.04878 0.60554 -0.081 0.936138
M2 -1.32671 0.63552 -2.088 0.042280 *
M3 -2.49279 0.63486 -3.926 0.000281 ***
M4 -2.97886 0.63440 -4.696 2.34e-05 ***
M5 -3.40494 0.63414 -5.369 2.39e-06 ***
M6 -3.95101 0.63407 -6.231 1.20e-07 ***
M7 -4.69709 0.63420 -7.406 1.98e-09 ***
M8 -4.42316 0.63452 -6.971 9.05e-09 ***
M9 -5.26923 0.63504 -8.298 9.19e-11 ***
M10 -5.17531 0.63575 -8.141 1.57e-10 ***
M11 -0.79393 0.63129 -1.258 0.214738
t -0.03393 0.01113 -3.047 0.003785 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.998 on 47 degrees of freedom
Multiple R-squared: 0.8913, Adjusted R-squared: 0.8612
F-statistic: 29.63 on 13 and 47 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.04235002 0.08470004 0.95764998
[2,] 0.08352898 0.16705796 0.91647102
[3,] 0.36447485 0.72894970 0.63552515
[4,] 0.80408809 0.39182382 0.19591191
[5,] 0.85150344 0.29699313 0.14849656
[6,] 0.78883635 0.42232729 0.21116365
[7,] 0.84400830 0.31198340 0.15599170
[8,] 0.92634318 0.14731365 0.07365682
[9,] 0.93648977 0.12702047 0.06351023
[10,] 0.91738284 0.16523432 0.08261716
[11,] 0.87763594 0.24472813 0.12236406
[12,] 0.84180589 0.31638822 0.15819411
[13,] 0.77447530 0.45104941 0.22552470
[14,] 0.69249552 0.61500897 0.30750448
[15,] 0.62176118 0.75647764 0.37823882
[16,] 0.58412890 0.83174220 0.41587110
[17,] 0.53229262 0.93541477 0.46770738
[18,] 0.50836043 0.98327914 0.49163957
[19,] 0.51657541 0.96684917 0.48342459
[20,] 0.47206632 0.94413264 0.52793368
[21,] 0.40159694 0.80319387 0.59840306
[22,] 0.36500410 0.73000820 0.63499590
[23,] 0.31586562 0.63173125 0.68413438
[24,] 0.39548898 0.79097796 0.60451102
[25,] 0.46458508 0.92917016 0.53541492
[26,] 0.70569270 0.58861459 0.29430730
[27,] 0.79525082 0.40949836 0.20474918
[28,] 0.66728254 0.66543493 0.33271746
> postscript(file="/var/www/html/rcomp/tmp/12s5r1227527151.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26x2i1227527151.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3mew91227527151.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xonf1227527151.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55ewe1227527151.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5
0.3864754098 0.2983333333 0.1983333333 0.2183333333 -0.3216666667
6 7 8 9 10
-0.8416666667 -1.4616666667 -2.6016666667 -2.1216666667 -1.0816666667
11 12 13 14 15
0.3708743169 1.3108743169 1.0935792350 0.7054371585 0.2054371585
16 17 18 19 20
0.2254371585 -0.1145628415 0.3654371585 0.8454371585 1.0054371585
21 22 23 24 25
0.9854371585 0.5254371585 -0.3220218579 -0.3820218579 0.0006830601
26 27 28 29 30
0.1125409836 0.7125409836 0.9325409836 0.3925409836 0.4725409836
31 32 33 34 35
0.1525409836 0.3125409836 0.3925409836 0.2325409836 -0.5149180328
36 37 38 39 40
-0.2749180328 0.1077868852 -0.7803551913 -0.8803551913 -1.6603551913
41 42 43 44 45
-1.0003551913 -1.0203551913 -0.1403551913 1.1196448087 1.2996448087
46 47 48 49 50
0.5396448087 1.8294808743 0.6694808743 -0.3478142077 -0.3359562842
51 52 53 54 55
-0.2359562842 0.2840437158 1.0440437158 1.0240437158 0.6040437158
56 57 58 59 60
0.1640437158 -0.5559562842 -0.2159562842 -1.3634153005 -1.3234153005
61
-1.2407103825
> postscript(file="/var/www/html/rcomp/tmp/6n4ah1227527151.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 0.3864754098 NA
1 0.2983333333 0.3864754098
2 0.1983333333 0.2983333333
3 0.2183333333 0.1983333333
4 -0.3216666667 0.2183333333
5 -0.8416666667 -0.3216666667
6 -1.4616666667 -0.8416666667
7 -2.6016666667 -1.4616666667
8 -2.1216666667 -2.6016666667
9 -1.0816666667 -2.1216666667
10 0.3708743169 -1.0816666667
11 1.3108743169 0.3708743169
12 1.0935792350 1.3108743169
13 0.7054371585 1.0935792350
14 0.2054371585 0.7054371585
15 0.2254371585 0.2054371585
16 -0.1145628415 0.2254371585
17 0.3654371585 -0.1145628415
18 0.8454371585 0.3654371585
19 1.0054371585 0.8454371585
20 0.9854371585 1.0054371585
21 0.5254371585 0.9854371585
22 -0.3220218579 0.5254371585
23 -0.3820218579 -0.3220218579
24 0.0006830601 -0.3820218579
25 0.1125409836 0.0006830601
26 0.7125409836 0.1125409836
27 0.9325409836 0.7125409836
28 0.3925409836 0.9325409836
29 0.4725409836 0.3925409836
30 0.1525409836 0.4725409836
31 0.3125409836 0.1525409836
32 0.3925409836 0.3125409836
33 0.2325409836 0.3925409836
34 -0.5149180328 0.2325409836
35 -0.2749180328 -0.5149180328
36 0.1077868852 -0.2749180328
37 -0.7803551913 0.1077868852
38 -0.8803551913 -0.7803551913
39 -1.6603551913 -0.8803551913
40 -1.0003551913 -1.6603551913
41 -1.0203551913 -1.0003551913
42 -0.1403551913 -1.0203551913
43 1.1196448087 -0.1403551913
44 1.2996448087 1.1196448087
45 0.5396448087 1.2996448087
46 1.8294808743 0.5396448087
47 0.6694808743 1.8294808743
48 -0.3478142077 0.6694808743
49 -0.3359562842 -0.3478142077
50 -0.2359562842 -0.3359562842
51 0.2840437158 -0.2359562842
52 1.0440437158 0.2840437158
53 1.0240437158 1.0440437158
54 0.6040437158 1.0240437158
55 0.1640437158 0.6040437158
56 -0.5559562842 0.1640437158
57 -0.2159562842 -0.5559562842
58 -1.3634153005 -0.2159562842
59 -1.3234153005 -1.3634153005
60 -1.2407103825 -1.3234153005
61 NA -1.2407103825
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.2983333333 0.3864754098
[2,] 0.1983333333 0.2983333333
[3,] 0.2183333333 0.1983333333
[4,] -0.3216666667 0.2183333333
[5,] -0.8416666667 -0.3216666667
[6,] -1.4616666667 -0.8416666667
[7,] -2.6016666667 -1.4616666667
[8,] -2.1216666667 -2.6016666667
[9,] -1.0816666667 -2.1216666667
[10,] 0.3708743169 -1.0816666667
[11,] 1.3108743169 0.3708743169
[12,] 1.0935792350 1.3108743169
[13,] 0.7054371585 1.0935792350
[14,] 0.2054371585 0.7054371585
[15,] 0.2254371585 0.2054371585
[16,] -0.1145628415 0.2254371585
[17,] 0.3654371585 -0.1145628415
[18,] 0.8454371585 0.3654371585
[19,] 1.0054371585 0.8454371585
[20,] 0.9854371585 1.0054371585
[21,] 0.5254371585 0.9854371585
[22,] -0.3220218579 0.5254371585
[23,] -0.3820218579 -0.3220218579
[24,] 0.0006830601 -0.3820218579
[25,] 0.1125409836 0.0006830601
[26,] 0.7125409836 0.1125409836
[27,] 0.9325409836 0.7125409836
[28,] 0.3925409836 0.9325409836
[29,] 0.4725409836 0.3925409836
[30,] 0.1525409836 0.4725409836
[31,] 0.3125409836 0.1525409836
[32,] 0.3925409836 0.3125409836
[33,] 0.2325409836 0.3925409836
[34,] -0.5149180328 0.2325409836
[35,] -0.2749180328 -0.5149180328
[36,] 0.1077868852 -0.2749180328
[37,] -0.7803551913 0.1077868852
[38,] -0.8803551913 -0.7803551913
[39,] -1.6603551913 -0.8803551913
[40,] -1.0003551913 -1.6603551913
[41,] -1.0203551913 -1.0003551913
[42,] -0.1403551913 -1.0203551913
[43,] 1.1196448087 -0.1403551913
[44,] 1.2996448087 1.1196448087
[45,] 0.5396448087 1.2996448087
[46,] 1.8294808743 0.5396448087
[47,] 0.6694808743 1.8294808743
[48,] -0.3478142077 0.6694808743
[49,] -0.3359562842 -0.3478142077
[50,] -0.2359562842 -0.3359562842
[51,] 0.2840437158 -0.2359562842
[52,] 1.0440437158 0.2840437158
[53,] 1.0240437158 1.0440437158
[54,] 0.6040437158 1.0240437158
[55,] 0.1640437158 0.6040437158
[56,] -0.5559562842 0.1640437158
[57,] -0.2159562842 -0.5559562842
[58,] -1.3634153005 -0.2159562842
[59,] -1.3234153005 -1.3634153005
[60,] -1.2407103825 -1.3234153005
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.2983333333 0.3864754098
2 0.1983333333 0.2983333333
3 0.2183333333 0.1983333333
4 -0.3216666667 0.2183333333
5 -0.8416666667 -0.3216666667
6 -1.4616666667 -0.8416666667
7 -2.6016666667 -1.4616666667
8 -2.1216666667 -2.6016666667
9 -1.0816666667 -2.1216666667
10 0.3708743169 -1.0816666667
11 1.3108743169 0.3708743169
12 1.0935792350 1.3108743169
13 0.7054371585 1.0935792350
14 0.2054371585 0.7054371585
15 0.2254371585 0.2054371585
16 -0.1145628415 0.2254371585
17 0.3654371585 -0.1145628415
18 0.8454371585 0.3654371585
19 1.0054371585 0.8454371585
20 0.9854371585 1.0054371585
21 0.5254371585 0.9854371585
22 -0.3220218579 0.5254371585
23 -0.3820218579 -0.3220218579
24 0.0006830601 -0.3820218579
25 0.1125409836 0.0006830601
26 0.7125409836 0.1125409836
27 0.9325409836 0.7125409836
28 0.3925409836 0.9325409836
29 0.4725409836 0.3925409836
30 0.1525409836 0.4725409836
31 0.3125409836 0.1525409836
32 0.3925409836 0.3125409836
33 0.2325409836 0.3925409836
34 -0.5149180328 0.2325409836
35 -0.2749180328 -0.5149180328
36 0.1077868852 -0.2749180328
37 -0.7803551913 0.1077868852
38 -0.8803551913 -0.7803551913
39 -1.6603551913 -0.8803551913
40 -1.0003551913 -1.6603551913
41 -1.0203551913 -1.0003551913
42 -0.1403551913 -1.0203551913
43 1.1196448087 -0.1403551913
44 1.2996448087 1.1196448087
45 0.5396448087 1.2996448087
46 1.8294808743 0.5396448087
47 0.6694808743 1.8294808743
48 -0.3478142077 0.6694808743
49 -0.3359562842 -0.3478142077
50 -0.2359562842 -0.3359562842
51 0.2840437158 -0.2359562842
52 1.0440437158 0.2840437158
53 1.0240437158 1.0440437158
54 0.6040437158 1.0240437158
55 0.1640437158 0.6040437158
56 -0.5559562842 0.1640437158
57 -0.2159562842 -0.5559562842
58 -1.3634153005 -0.2159562842
59 -1.3234153005 -1.3634153005
60 -1.2407103825 -1.3234153005
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7ndte1227527151.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8x5wq1227527151.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9803z1227527151.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109llz1227527151.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1122ri1227527151.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/121aym1227527151.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1375rg1227527152.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14lj8g1227527152.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15xq3x1227527152.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16theh1227527152.tab")
+ }
>
> system("convert tmp/12s5r1227527151.ps tmp/12s5r1227527151.png")
> system("convert tmp/26x2i1227527151.ps tmp/26x2i1227527151.png")
> system("convert tmp/3mew91227527151.ps tmp/3mew91227527151.png")
> system("convert tmp/4xonf1227527151.ps tmp/4xonf1227527151.png")
> system("convert tmp/55ewe1227527151.ps tmp/55ewe1227527151.png")
> system("convert tmp/6n4ah1227527151.ps tmp/6n4ah1227527151.png")
> system("convert tmp/7ndte1227527151.ps tmp/7ndte1227527151.png")
> system("convert tmp/8x5wq1227527151.ps tmp/8x5wq1227527151.png")
> system("convert tmp/9803z1227527151.ps tmp/9803z1227527151.png")
> system("convert tmp/109llz1227527151.ps tmp/109llz1227527151.png")
>
>
> proc.time()
user system elapsed
2.450 1.554 3.027